提升文本搜人准确率，让模糊描述也能精准匹配行人图像。
CONQUER: Context-Aware Representation with Query Enhancement for Text-Based Person Search
- 分阶段训练增强跨模态对齐，提升图文特征一致性。
- 推理时自动优化模糊查询，效果比基线高10%以上。
- 适合实际场景中不完整或含糊的文本搜索需求。
文本驱动的人体搜索（TBPS）旨在通过自然语言描述从大规模图库中检索行人图像，对公共安全至关重要，但受限于模态差异和用户查询模糊性。本文提出CONQUER，一种两阶段框架：训练阶段采用多粒度编码、互补样本挖掘与基于最优传输的上下文引导最优匹配，学习鲁棒嵌入；推理阶段引入即插即用的查询增强模块，通过锚点选择与属性驱动补全，无需重训练主干网络即可优化模糊或不完整的查询。在CUHK-PEDES、ICFG-PEDES和RSTPReid数据集上的实验表明，CONQUER在Rank-1和mAP指标上持续超越强基线，尤其在跨域和不完整查询场景下表现显著。结果验证了其在真实环境部署中的有效性。源代码已公开于https://github.com/zqxie77/CONQUER。
原文摘要 · Abstract (English)
Text-Based Person Search (TBPS) aims to retrieve pedestrian images from large galleries using natural language descriptions. This task, essential for public safety applications, is hindered by cross-modal discrepancies and ambiguous user queries. We introduce CONQUER, a two-stage framework designed to address these challenges by enhancing cross-modal alignment during training and adaptively refining queries at inference. During training, CONQUER employs multi-granularity encoding, complementary pair mining, and context-guided optimal matching based on Optimal Transport to learn robust embeddings. At inference, a plug-and-play query enhancement module refines vague or incomplete queries via anchor selection and attribute-driven enrichment, without requiring retraining of the backbone. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate that CONQUER consistently outperforms strong baselines in both Rank-1 accuracy and mAP, yielding notable improvements in cross-domain and incomplete-query scenarios. These results highlight CONQUER as a practical and effective solution for real-world TBPS deployment. Source code is available at https://github.com/zqxie77/CONQUER.
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